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This research introduces AlignGAD, a zero-shot framework designed to detect anomalies across unseen graph domains by aligning heterogeneous features in the spectral domain. By leveraging node reconstruction discrepancy as an anomaly score, the method addresses critical generalization limitations inherent in current cross-domain approaches for machine learning applications.
A Zero-shot Generalized Graph Anomaly Detection Framework via Node Reconstruction
arXiv:2606.12673v1 Announce Type: cross Abstract: Cross-domain graph anomaly detection (GAD) aims to identify abnormal nodes in unseen target graphs, showing strong potential in real-world applications with heterogeneous graph data. However, existing methods often depend on dataset-specific feature semantics and structural patterns, which limits their ability to generalize across different domains. To address this challenge, we propose AlignGAD, a zero-shot generalized graph anomaly detection framework. Our framework is built upon three key components: a Global Unification Module that aligns heterogeneous node features and normalizes graph signals in the spectral domain; a Clustering Module that constructs cluster-aware graph views to capture group-level abnormal patterns; and a Node Discrepancy Scoring Module that measures reconstruction discrepancy and aggregates anomaly evidence from different graph views. Experiments on multiple real-world datasets demonstrate the effectiveness of AlignGAD under the zero-shot GAD setting.
Abstract:Cross-domain graph anomaly detection (GAD) aims to identify abnormal nodes in unseen target graphs, showing strong potential in real-world applications with heterogeneous graph data. However, existing methods often depend on dataset-specific feature semantics and structural patterns, which limits their ability to generalize across different domains. To address this challenge, we propose AlignGAD, a zero-shot generalized graph anomaly detection framework. Our framework is built upon three key components: a Global Unification Module that aligns heterogeneous node features and normalizes graph signals in the spectral domain; a Clustering Module that constructs cluster-aware graph views to capture group-level abnormal patterns; and a Node Discrepancy Scoring Module that measures reconstruction discrepancy and aggregates anomaly evidence from different graph views. Experiments on multiple real-world datasets demonstrate the effectiveness of AlignGAD under the zero-shot GAD setting.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2606.12673 [cs.LG]
(or arXiv:2606.12673v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.12673 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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